JMIR Publications

ABSTRACT

Background:

The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, big cities and rural areas, and diverse neighborhoods within the same cities. The absence of a national strategy in battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies.

Objective:

This study examines the content of the tweets sent by public health agencies in Texas about COVID-19 and how such content predicts the level of public engagement.

Methods:

All COVID-19 related tweets (n=7269) posted by Texas public agencies were downloaded. These tweets were classified in terms of each tweet’s functions (whether the tweet provides information, promotes action, or builds community), preventative measures mentioned, and health beliefs discussed using natural language processing. Hierarchical linear regressions were run to explore how tweet content predicted public engagement.

Results:

Information was the most prominent function, followed by action and community. Susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets serving the action function was most likely to be retweeted, while tweets performing the action and community functions were more likely to be liked. Tweets communicating susceptibility and severity information led to more public engagement.

Conclusions:

Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve social media message strategies regarding the benefit of disease prevention behaviors.